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.0em plus.0ex minus.2ex-.5emTraining Resource Benchmark. We explore the benefits of frozen VLMs in terms of training resource savings. We benchmark with ViLD [1]} as it is most comparable to F-VLM. Both adopt the same Mask R-CNN head configuration and training recipe [2]}, and neither require detection-tailored pretraining. We follow ViLD and compare the training cost on TPUv3 cores on the same batch size. The data about ViLD training time and resource use is obtained directly from the authors [1]}. To keep the benchmark simple, we assume the pretrained VLMs are given and exclude their training costs from the comparison. For F-VLM, we use the R50x64 backbone and report the average over 5 independent runs.
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